While computer vision remains a complex problem in artificial intelligence, recent achievements such as the recursive cortical network (RCN) have enabled computers to identify objects from visual data efficiently and with high accuracy. However, just as with human vision, object recognition is only a part of the skillset needed to effectively interact with an environment. Humans observe how objects interact with each other to infer properties of those objects; for example, by observing how a sphere reacts when dropped onto a hard surface, a human may be able to infer whether a ball is made of rubber, cork, or steel. This knowledge makes it easier to accurately interpret past events, and likewise, to predict future events.
Unfortunately, traditional approaches to computer vision are often inefficient at modeling the latent properties of objects observed from visual data. Thus, there is a need in the artificial intelligence field to create new and useful systems and methods for event prediction using schema networks. This invention provides such new and useful systems and methods.